Policymakers in different Countries have introduced different political action to contrast the COVID19 contagion.

1. What are the different containment efforts and is there a strategies resemblance across countries?

2. What is the effect of these policies on the contagion from a global perspective?

3. Has the same action lead to different results in the case of different regions of Italy?

• COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for contagion data,

• Oxford COVID-19 Government Response Tracker (OxCGRT) for policies tracking.

• Dimension Reduction via Polychoric PCA for \(11\) ordinal variables (from \(0\) to \(2\) or \(0\) to \(3\)) indicating the stringency level of policies such as

  • School, workplace and transport closing and event cancellation;

  • Gathering, stay-home and internal/international movement restrictions;

  • Information, Testing and Contact Tracing campaigns.

• Functional Data Co-Clustering of the countries aligned to the first contagion (from the 10th day before contagion).

Restriction-based policies on one hand, Tracing and Testing policies on the other hand.

WHY?

• We analyze the number of Active person, i.e., Confirmed - Deaths - Recovered, \(14\) days after lockdown policies application\(\rightarrow\) Count Dependent Variable (Generalized Poisson Model);

• The data are observed for each country nested within clusters during \(131\) days \(\rightarrow\) Mixed Model.

• Lockdown policies work! respect to impose no measure;

• Strong Testing and Tracing policies lead to discovering more infected people (luckily!)

• Korea and Singapore are the best countries that acted properly;

• Sweden, Germany, Portugal, and Greece better than the other UE countries;

• The USA, and Canada better than the other UE countries except for Sweden and Germany.

• Italian regions, ethernal divide

• Lockdown almost simultaneous, excepted the Red Zone

• First cases in Lombardia and Lazio hubs

• Policies have no variability between regions

• Baseline control: some regions start from worse situations

• Cannot estimates some effects as for the nations case

• To our defense, integration between databases came lately

• Instrumental variables, more correct but tricky approach

• Phase “1” versus Phase “0” comparison

• Auto-regression: modelling active cases given past numbers
  • Related but not quite to the R0 index

• Random effects for region and date, standard panel approach

• Assuming policies effects seen ~14 days later

• Controlling for testing frequency